code: def PaLM(*, dim, num_tokens, depth, dim_head=64, heads=8, ff_mult=4):
      net = nn.Sequential(
          nn.Embedding(num_tokens, dim),
          *[
              ParallelResidual(
                  Attention(dim=dim, dim_head=dim_head, heads=heads),
                  FeedForward(dim=dim, mult=ff_mult),
              )
              for _ in range(depth)
          ],
          LayerNorm(dim),
          nn.Linear(dim, num_tokens, bias=False),
      )
  
      # they used embedding weight tied projection out to logits, not common, but works
      net[-1].weight = net[0].weight
  
      nn.init.normal_(net[0].weight, std=0.02)
      return net
  
imports: import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import matmul, nn
label_desc: Use image node_id 4 for calling a global function with example usage: PaLM() and returns: net